Web3 Firehose

ABM is back. And it's all about signals. There are 3 layers of signals every GTM team in 2026 should track: 1️⃣ 1st-Party Signals (Data you own) → CRM- Data already in your CRM. [HubSpot, Salesforce, Attio] → Product Usage- Product analytics triggers. [Mixpanel, Amplitude, Intercom] → Meeting Forms- Demo bookings or form drop-offs. [Chili Piper, Default, Calendly] → Gated Content- Opt-in content/lead magnets. [Webflow, Distribute.so, Gamma] → Marketing Sequences- Engagement with email flows. [beehiiv, Customer.io, Kit] → Webinar Attendance- Attendees of online events. [Luma, LinkedIn events, Goldcast] → Website Visitors- Deanonymized web traffic. [Instantly.ai, Warmly, RB2B] → Outreach Replies- Sales engagement tool prospects. [Instantly.ai, HeyReach.io, OutboundSync] 2️⃣ 2nd-Party Signals (Exclusive data from external tools) → Partner Signals- Shared data from integration partners. [Crossbeam, PartnerStack, WorkSpan] → Warm Intros- Common connections with prospects. [Commsor, The Swarm 🔆, LinkedIn] → Review Sites- Intent from software review sites. [G2, Capterra, TrustRadius] → Champion Tracking- Previous customer job changes. [LoneScale, Champify, Clay] → Ad Insights- Engagement from ad campaigns. [ZenABM, Fibbler, Vector 👻] → LinkedIn Engagement- Interactions on LI posts. [Clay, Jungler, Teamfluence™] 3️⃣ 3rd-Party Signals (Public data from external sources) → Technographic Data- Changes in tech stack. [BuiltWith, HG Insights, TheirStack] → Job Openings- New postings on job platforms. [Clay, PredictLeads, People Data Labs] → Firmographic Data- Company demographic changes. [Apollo, AI Ark, BetterContact] → People Data- Staffing changes. [Findymail, Clay, Apollo] → Job Changes - New hires. [Sales Nav, Clay, Apollo] → News- Company announcements. [Clay, Google News, Perplexity] → Advertisement Activity- Media buying behavior. [Adyntel, Adbeat, Apify] → Web Data- Data scraped from the web. [Clay, Apify, ZenRows] → Social Signals- Engagement on socials. [Clay, PhantomBuster, Jungler] → Search Analytics- SEO changes. [SemRush, Similarweb, Ahrefs] → Funding- Recent fundraising events. [Crunchbase, Owler, Pitchbook] ___ Every marketer wants to know: - Who knows they have a problem? - Who already knows our company? - Who's actively considering buying? Signal stacking is the closest thing we have to answer these questions. Follow Fivos Aresti for more GTM playbooks. | 29 comments on LinkedIn

B2B marketing services are at an inflection point. What was once a fragmented ecosystem of creative agencies, demand-gen shops, digital firms, marketing automation consultants, and analytics boutiques is colliding with a new reality: buyers now expect full-funnel precision, speed, measurable revenue

Every board meeting I attend now starts with the same question:

Early on, you can often just sell a service as your "MVP" - rather than spending a bunch of time building a product that people may or may not want.

From industry-standard to open-source freedom

I’ve been disappointed with HBR articles on LLMs of late and been openly critical. This one’s a notable corrective. Credit where it’s due, The Gen AI Playbook for Organizations is a far more credible, grounded lens. It’s aware of epistemic limits in a way most recent HBR AI pieces simply haven’t been. The authors introduce two genuinely useful axes - cost of errors and type of knowledge (explicit vs tacit) - to decide where Gen AI can be used well. That simple distinction reframes the whole conversation from “how smart is the model?” to “what kind of knowing does this task demand, and what’s the price of being wrong?” It’s still classic HBR - managerial tone, quadrant chart, actionable checklists - but it earns the framework. Think of it as McKinsey meets tacit knowledge, competently. What it gets right: - Reintroduces human judgment: empathy, ethical reasoning, and context as irreducible. - Offers a practical map of where Gen AI belongs today and where it doesn’t. - Frames the paradox of access: when everyone can use the same tools, advantage shifts to how you use them. - Grounds strategy in difference, not speed. What leaders can take from it: If you’re wrestling with Gen AI adoption, three questions here are worth carrying into the boardroom: 1️) Where is the cost of being wrong low enough to let Gen AI run? Start there. Don’t chase perfection; chase learning. 2️) Which tasks rely on tacit judgment, context, or ethics? Keep humans squarely in the loop, that’s where trust and reputation live. 3️) How can we use the time Gen AI saves to improve coordination, not just efficiency? Free hours mean nothing if feedback loops stay broken. Those three prompts alone could help many reshape how leadership teams approach pilots, governance, and ROI. The piece reveals where corporate AI thinking still sits, caught between epistemic humility and managerial control. Where it falls short: - They echo Polanyi without ever naming him, invoking tacit knowledge while designing as if it could be made explicit. - They say strategy beats speed, then prescribe speed as strategy. - Their “cost of errors” model assumes organisations know that cost, when it’s often emergent, reputational, or discovered too late. - They keep humans in the loop but forget humans are the loop, the source of coherence itself. That’s where it overlaps - and diverges - from my own positioning. Where they speak of knowledge types, I speak of rails: the organisational feedback systems, semantics, and judgment structures that turn intelligence into ROI. Where they stop at “human oversight,” I push toward management discipline over model fascination. Still, it’s a step in the right direction, for once, an HBR article on AI that recognises strategy is about where humans remain essential, not how fast machines improve. | 15 comments on LinkedIn

The AI consulting space is full of NOISE - but there’s a small group of firms actually doing BRILLIANT work. Here’s who I’d keep on your radar 👇 Quantum Rise – I'm a big fan of their “Consulting 2.0” mantra: profit, not PowerPoints - frankly, the entire industry needs to move in this direction, but that's another rant for another time. Alex Kelleher (former Deloitte Global CMO and serial entrepreneur) has built one of the earlier - and larger - players in this space with cross-industry impact, especially for companies that operate in the "physical world". Tenex – Alex Lieberman made waves in the consulting community earlier this year when he announced his intent for Tenex to be the “McKinsey for AI.” They're a powerful engineering shop for sure, and I’m particularly impressed by how an “outsider” (aka, not founded by a former consultant) approached this. Goes to show there’s real diversity of thought in this space, and a ton for the rest of us to learn too. Casper Studios – I’ve admired these guys for a while now, largely for their versatility, working with clients from hedge funds to nonprofits to creators. One of the best design-oriented build firms I’ve seen. Jay Singh's approach to design-driven AI is something I’ve learned a lot from. Morningside AI – Honestly, I didn’t think a firm born from a YouTube influencer would ever stand ground against “established” firms - but they’re quickly ceding it. A testament to the team and the credibility they’ve built. Based in New Zealand, recently brought on Nick Roco (another one to watch) to head up their consulting arm. Pathfindr – Also from “the land down unda” (can I even say that?), I’ve been amazed by their practical, anti-consulting approach to AI. They truly make it SIMPLE - exactly what leaders want. Dawid Naude (ex-ACN) has been building something special, and it shows: they were recently acquired by the Affinda Group, a testament to their concept + execution. AI Momentum Partners (AMP) – Word on the street is they have the coolest, funniest, smartest, most handsome founder on the block (Just kidding - I’m way more self-deprecating than that!). AMP is among the fastest-growing strategy-to-execution partners in the AI consulting arena, leveraging an "MBB meets FAANG" approach across service-oriented mid-market and private equity-backed firms. Superposition - Not really an “AI consulting firm” (but rather, a strategy partner for Data & AI companies). Honestly, I just like David Cohen. Sooo... who did I miss? Tag the founders and firms you think are ACTUALLY pushing the AI consulting industry forward - I’ll feature a few in a follow-up post next week. :) #AI #Consulting #AIConsulting #MBB #Big4 #DigitalTransformation | 172 comments on LinkedIn

AI will replace jobs and do so quickly... Anthropic released a study on Monday that showed most businesses (77%) are using its Claude chatbot to automate work. Only 12% of businesses used Claude as a collaborative or learning tool. The report stated that the trend towards automation could “bring disruption in labor markets” and cause workers to lose their jobs And today from Digiday... S4 Capital has dropped the pretense. The company’s top brass are now openly framing AI agents not as enhancement to agency work, but as the replacement. In their view, the traditional agency model isn’t evolving – it’s being automated out. And that automation is already reshaping how ads get made. Clients like Google are now paying S4 for what its AI agents can deliver, not its people. Take a recent Pixel smartphone campaign. AI handled nearly every part of the production pipeline: scriptwriting, storyboarding, even directorial decisions. Pre-production, production, post – all generated by agents. What would’ve taken months with a traditional team was compressed into weeks. “We personally estimate about 65% of the tasks agencies get paid for currently could be done by AI agents with today’s technology,” said Wes ter Haar, an executive director of S4 Capital on the earnings call for the group’s interim results. It’s the quiet part said out loud. https://lnkd.in/g43YqC32 | 19 comments on LinkedIn

A few #martech and #ecosystem observations on Snowflake's just-released Modern Marketing Data Stack 2026 report: 1️⃣ Cloud data warehouses/lakehouses continue to gain momentum as a universal data layer, driving improved data integration across the tech stack. It isn't a panacea, as there's still work required with good cross-org data design and governance. Still many a Big Ops challenge on top of Big Data. But it's a solid, open foundation to build upon. 2️⃣ Snowflake has one of the best aligned platform ecosystem models in tech today. They make money from their customers consuming compute and storage resources. The more 3rd-party applications a customer integrates with Snowflake, the more resources consumed and billed. Customers only connect and keep using apps that they find valuable. The integration between the app and Snowflake make both more valuable (more data flow = more value). There's near perfect alignment in incentives and rewards between Snowflake, its integration partners, and their mutual customers. The hyperscalers (AWS, GCP, Azure) have similar ecosystem alignment. But it's still a rare pattern among SaaS application platforms, which often rely on marketplace transaction fees, rev share payments, program fees, or indirect measures of value to drive tech partnerships. While those non-consumption partnership models are workable, they often risk "drift" in their alignment. 3️⃣ What's remarkable about Snowflake's Modern Marketing Data Stack illustration below is its relatively good understanding of martech categories and players connected to their platform. Snowflake is not a martech company per se, but this representation of a martech ecosystem is better than I've seen from most pure-play martech platforms. Of course, it's not perfect — I could certainly nitpick logos and categories for hours (it's my hobby). And I assume there's bias around featured partners. But overall, this is a reference example of how a broad infrastructure provider can do excellent partner marketing in a domain. 4️⃣ I love that service providers are represented on this tech ecosystem map too. There's such a powerful interplay between them, but they're rarely shown together in the same context. The blurring boundaries between software and services with AI is only going to make that more of an entangled ecosystem. (Nod to Jay McBain and his 7 partners on average who influence a deal.) Good stuff! | 46 comments on LinkedIn

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🚨 Introducing the AI Apps 50: Startup Edition Ever wondered how startups are spending their money when it comes to AI? Our team at Andreessen Horowitz worked with Mercury to crunch the numbers and rank the top applications by spend. The list + what we learned from it ⬇️ - Horizontal apps have a slight lead over vertical (60% of the list). This includes general assistants (ex. Perplexity) and SIX different meeting support tools (ex. Fyxer AI). But, it also encompasses creative tools and vibe coding tools that are used in roles across orgs. - Vertical apps can augment human labor...or replace it. We're mostly seeing the former - but five companies on the list allow customers to "hire AI" (ex. Crosby Legal, Cognition, 11x). Labor augmenters mostly assist with customer service, sales, and recruiting. - Vibe coding has landed in enterprises. It's not just a prosumer trend! Number three on the list, below OpenAI and Anthropic? Replit. Other listmakers in the category include Lovable and Emergent, while Cursor made the ranks for more technical users. - Products are making the consumer - enterprise jump. 12 cos also appeared in our most recent Consumer AI Top 100 - almost all of which started out B2C and have migrated B2B over time. In fact, 70% of listmakers are available for individual use (no enterprise license needed)! Check out the full report: https://lnkd.in/gmMvfvSv | 15 comments on LinkedIn

Ah, the Hype Cycle. No other curve is both so revered and reviled. When it comes to generative AI's Hype Cycle, especially its application in marketing, there are three things we need to acknowledge: First, 'gen AI' is not a single technology moving along the Hype Cycle. Is it at the peak of inflated expectations? The trough of disillusionment? The slope of enlightenment? The answer is: all of the above. Different gen AI applications and

Cost per token is the WRONG metric to focus on. As businesses integrate generative AI into real workflows, they are facing rising costs due ...

Super insightful map of AI agent tech stack categories by CB Insights with plenty of good examples of vendors/products in each. #martech | 12 comments on LinkedIn

Uncover 50+ AI marketing statistics the extent of AI adoption in the industry, its benefits, and statistics that predict the future of AI and marketing.

In 2025, AI will have firmly established itself as an essential driver of success for marketing agencies worldwide. No longer simply a futuristic possibility, AI has become an operational necessity, fundamentally altering how agencies execute campaigns, develop creative content, and deliver measurab

Revenue Labs helps B2B companies build modern GTM systems. We connect tech stacks and AI with the self-learning Contextual Intelligence needed to drive better decision-making, more effective workflows and faster execution.

🚨 AI in GTM isn’t a feature. It’s a system. And it's your strategic GTM moat. Most companies don’t have it. They’re still running SaaS-native GTM. 📉 The SaaS-native playbook: CRMs, call recorders, sequences, dashboards CDPs offering “Infrastructure” = storing more structured data Works when the market felt steady and predictable. But today's world is different - and we need to adapt to the pace: → AI is rewriting business models and operations → Customer behaviour is shifting → Decisions need to be made in real-time And the SaaS-native GTM reaction? They bolt AI on top. → Chat-to-CRM. → Write emails. → Summarise calls. And they wonder why nothing changes. → More activity ≠ better outcomes → Automation ≠ intelligence → Dashboards ≠ decisions 🧠 AI-native GTM starts with infrastructure. GTM Intelligence Infrastructure - the 5-layer stack that gives AI the context + intelligence + memory to decide → act → learn. 1️⃣ The Data Layer Capture every GTM signal in real time. Structured + unstructured. Every company update, social signal, product event, call, email, support ticket. → One unified stream of truth. 2️⃣ The Context Layer Apply GTM-specific semantics: ICPs, personas, product, customer journey, sales processes etc. → Turn raw data into shared understanding across systems. 3️⃣ The Memory Layer Persistent, GTM-wide awareness. → Every account’s history, interactions, decisions - what worked, what failed. 4️⃣ The Intelligence Layer The decision engine. Score, prioritise, predict and simulate GTM moves in real time. 5️⃣ The Execution Layer 200+ GTM Agents - specialists for Sales, CS, Marketing and Product - orchestrating workflows, triggering plays and compounding learning every cycle. Once these layers are in place. Raw → Context → Signal → Act → Learn - compounding intelligence every day. 🏰 This builds a GTM moat. → Every signal enriches context - accuracy improves with scale. → Every action updates memory - execution gets sharper each cycle. → Every win/loss refines intelligence - strategy evolves automatically. → 200+ agents share one brain - insights flow instantly across functions. If your GTM forgets - you start from zero every quarter. You can’t out-hire or out-dashboard this. This is why the companies winning this decade will be AI-native. And that starts with the right infrastructure. 📩 Interested in GTM Intelligence Infrastructure? DM me. ✉️ Building AI-native GTM? I'd love to hear more. Get in touch. 🔔 Follow for more AI-native GTM insights and product drops. | 11 comments on LinkedIn